Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data
During the last decade various algorithms have been developed and proposed for discovering overlapping clusters in high-dimensional data. The two most prominent application fields in this research, proposed independently, are frequent itemset mining (developed for market basket data) and biclustering (applied to gene expression data analysis). The common limitation of both methodologies is the limited applicability for very large binary data sets. In this paper we propose a novel and efficient method to find both frequent closed itemsets and biclusters in high-dimensional binary data. The method is based on simple but very powerful matrix and vector multiplication approaches that ensure that all patterns can be discovered in a fast manner.
Bittable_TID is a biclustering tool written in MATLAB. It provides a fast solution for finding all biclusters within a binary data matrix.
It is also described in:
A Király, A. Gyenesei, J. Abonyi, Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data, The Scientific World Journal, vol. 2014, Article ID 870406, 7 pages
You can download other software tools used for comparison and data sets from here:
http://www.abonyilab.com/biclustering
引用格式
Janos Abonyi (2024). Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data (https://www.mathworks.com/matlabcentral/fileexchange/47170-bit-table-based-biclustering-and-frequent-closed-itemset-mining-in-high-dimensional-binary-data), MATLAB Central File Exchange. 检索时间: .
MATLAB 版本兼容性
平台兼容性
Windows macOS Linux类别
标签
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!版本 | 已发布 | 发行说明 | |
---|---|---|---|
1.0.0.0 |